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CAFFEINE INTAKE AMONG LAW ENFORCEMENT OFFICERS PARTICIPATING IN SHIFT
WORK
ABSTRACT
Objective: To investigate caffeine consumption among law enforcement officers during day shifts (DAY)
and night shifts (NIGHT); to assess the types of caffeine-containing products and the frequency of
product intake during DAY and NIGHT; to identify relationships between caffeine products and selected
demographic characteristics; to identify relationships between caffeine intake and job-related
characteristics.; and to identify associative relationships between caffeine intake and perceived
concentration level, caffeine side effects, and tobacco use.
Participants: Police officers (PO) and Sheriff Department Deputies (SDD) in a rural region in Eastern
North Carolina. Useable sample of 75 (N = 75).
Methods: Anonymous, self-administered caffeine food-frequency questionnaires (FFQ; three for DAY,
three for NIGHT) and demographic questionnaire. Nonparametric tests were used. The Wilcoxon signed
rank test was used to compare related samples. Spearman’s correlation was used to determine
relationships between two samples. The Mann-Whitney U test was used to compare two independent
samples. The Kruskal-Wallis test was used to compare more than two independent samples.
Results: Caffeine consumption was similar from DAY to NIGHT (225 + 227mg DAY; 252 + 232 mg
NIGHT; p=0.891). Frequency of tea intake was greater during DAY (p=0.032). Greater caffeine
consumption was found among (1) SDD versus PO; (2) expert officers (for night only); (3) lower
concentration levels (for night only); (4) tobacco users; (5) those who experience caffeine side effects. In
addition, younger officers consumed more energy drinks DAY and NIGHT.
Conclusions: It was found that certain job-related characteristics influence caffeine intake, but types of
products used and frequency of intake tends to be the same regardless of shift or caffeine side effects.
Energy drinks were found to be most popular among younger officers. Tobacco use and caffeine intake
were found to have a correlate relationship. Caffeine may aid alertness, especially at night. Further
research is needed to assess other areas of shiftwork, more caffeine-containing products, especially
energy drinks, and psychological or behavioral aspects of caffeine intake and product choice among shift
workers. Limitations, gaps in the literature and implications are discussed.
CAFFEINE INTAKE AMONG LAW ENFORCEMENT OFFICERS PARTICIPATING IN
SHIFT WORK
A Thesis Submitted to
The Faculty of the Department of Nutrition and Dietetics
College of Human Ecology
East Carolina University
In Partial Fulfillment of the Requirements for the Degree
Master of Science in Nutrition
by
Maddison M. Greaves, November 2013
CAFFEINE INTAKE AMONG LAW ENFORCEMENT OFFICERS PARTICIPATING IN
SHIFT WORK
by
Maddison M. Greaves
APPROVED BY:
DIRECTOR OF THESIS: ________________________________________________________
Brenda M. Bertrand, PhD, RD
COMMITTEE MEMBER: _______________________________________________________
Christopher Duffrin, PhD
COMMITTEE MEMBER: _______________________________________________________
Xiangming Fang, PhD
COMMITTEE MEMBER: _______________________________________________________
Kimberly Myers, PhD, MHS, RD, LDN
CHAIR OF THE DEPARMENT OF NUTRITION AND DIETETICS:
________________________________________________________
William A. Forsythe III, PhD
DEAN OF THE GRADUATE SCHOOL: ___________________________________________
Paul J. Gemperline, PhD
I would like to give sincere gratitude to Dr. Bertrand and the members of my committee for their
guidance and insight throughout the completion of this work.
TABLE OF CONTENTS
LIST OF TABLES……………………………………………………………………………… vii
LIST OF FIGURES……………………………………………………………………………..viii
LIST OF ABBREVIATIONS…………………………………………………………………….ix
CHAPTER 1: REVIEW OF LITERATURE……………………………………………………...1
CHAPTER 2: MANUSCRIPT: CAFFEINE INTAKE AMONG LAW ENFORCEMENT
OFFICERS PARTICIPATING IN SHIFT WORK ………………………………………………7
Abstract……………………………………………………………………………………7
Introduction………………………………………………………………………………..9
Methods…………………………………………………………………………………..11
Caffeine Food Frequency Questionnaire………………………………………...11
Demographic and Caffeine Effects Questionnaire………………………............12
Procedures………………………………………………………………………..13
Statistical Analysis……………………………………………………………….13
Results……………………………………………………………………………............14
Assessment of Caffeine Consumption……………………………………...........14
Caffeine Product Use and Demographic Characteristics…………………...........15
Caffeine Intake and Job Related Characteristics………………………………...15
Associative Relationship between Caffeine Intake, Perceived Concentration Level,
Caffeine Side Effects and Tobacco Use………………………………………….16
Discussion………………………………………………………………………………..16
References………………………………………………………………………………..21
List of Tables…………………………………………………………………………….27
List of Figures……………………………………………………………………………28
REFERENCES…………………………………………………………………………………..41
APPENDIX A: IRB APPROVAL LETTER…………………………………………………….48
APPENDIX B: PERMISSION LETTERS………………………………………………………49
APPENDIX C: FOOD FREQUENCY QUESTIONNAIRE…………………………………….52
APPENDIX D: DEMOGRAPHIC AND CAFFEINE EFFECTS QUESTIONNAIRE………....53
APPENDIX E: SERVING SIZE REFERENCE SHEET………………………………………..54
LIST OF TABLES
TABLE 1. Mean caffeine content (mg) per serving of caffeine-containing products assessed
on the food frequency questionnaire as reported by various sources……………29
TABLE 2. Age and job-related characteristics of Police and Sheriff law enforcement
officers…………………………………………………………………………...30
TABLE 3. Demographic characteristics of Police and Sheriff law enforcement
officers…………………………………………………………………………...31
TABLE 4. Number of days or nights during a 3-day shift that caffeine-containing products were
consumed among Police and Sheriff officers as reported by food frequency
questionnaire……………………………………………………………………………..33
TABLE 5. Mean caffeine-containing product frequency during day and night shifts among Police
and Sheriff officers as reported by food frequency questionnaire……………………….34
TABLE 6. Mean distribution of ages for energy drink consumption during day and night shifts ….35
TABLE 7. Caffeine intake between Police and Sheriff departments for 3-day period of day and night
shifts as reported by food frequency questionnaire……………………………………...36
TABLE 8. Associative relationships between caffeine intake and tobacco use, caffeine side effects,
and perceived concentration level among Police and Sheriff officers…………………...37
LIST OF FIGURES
Figure 1. Caffeine intake during a 3-night shift period among “rookie,” “seasoned,” and “expert” Police
and Sheriff officers………………………………………………………………………38
Figure 2. Association of age and caffeine intake for those with caffeine side effects vs. those without side
effects for day shifts among Police and Sheriff officers ……………………………..39
Figure 3. Association of age and caffeine intake for those with caffeine side effects vs. those without side
effects for night shifts among Police and Sheriff officers……………………………….40
LIST OF ABBREVIATIONS
DAY Day shifts
NIGHT Night shifts
PO Police Officers
SDD Sheriff Department Deputies
SD Sheriff Department
FFQ Food Frequency Questionnaire
CHAPTER 1
LITERATURE REVIEW
Participation in shift work has been found to interfere with normal behaviors, including eating,
exercise, and sleep patterns, potentially leading to adverse health effects. Atkinson, Fullick, Grindey and
Maclaren (2008), reviewed the behavioral and biological disturbances associated with shift work. Shift
work has been linked with increased BMI, prevalence of obesity, and other health complications
(Atkinson, et. al., 2008). Total energy intake during shift work does not appear to be affected; however,
there is a tendency toward decreased meal frequency and increased snacking during night shift work
(Atkinson, et. al., 2008). It was concluded that a multidisciplinary approach to investigating health and
energy balance of shift workers is needed as the biological and behavioral factors influencing energy
expenditure and intake are not independent of each other (Atkinson, et. al., 2008).
Holmback and colleagues (2002) studied the postprandial reaction related to macronutrient
composition when food is consumed at different points during a 24-hour period. It was hypothesized in
this study that subjects would respond metabolically different when given identical meals in the morning
compared to the evening, glucose tolerance would decrease from morning to midnight, and night time
eating would increase low density lipoprotein to high density lipoprotein ratio (LDL:HDL; Holmback, et.
al., 2002). The nocturnal hormonal pattern was thought to influence these results, as well as contribute to
the prevalence of obesity and cardiovascular disease seen in shift workers (Holmback, et. al., 2002).
Results showed that participants’ basal metabolic rate (BMR) tended to be higher after day 6 of a
high fat diet. Fasting blood glucose, non-esterified fatty acids (NEFA), and glycerol concentrations did
not differ when given a high fat versus high carbohydrate diet, but the triglyceride concentration was
greater for the high carbohydrate diet (Holmback, et. al., 2002). It was determined that a high fat diet
impacted metabolic responses differently than a high carbohydrate diet, and these responses may put shift
workers at an increased risk for metabolic disturbances, including obesity, cardiovascular disease, and
gastrointestinal problems (Holmback, et. al., 2002).
2
De Assis, Kupek, Nahas and Bellisle (2003) studied the association between nutritional-and
health-related problems associated with shift workers, specifically garbage collectors of Florianopolis,
Brazil. Because of the increase in shift work to about 20% of the work force in industrialized countries, it
is important to understand the cause and effect of health issues related to shift work (De Assis, et. al.,
2003). There were no to date studies showing the influence of shift time on food intake in workers with
very high energy expenditure, thus, this study investigated energy and nutrient intake of garbage
collectors (a high energy job) based on meal recording during a 24-hour period (De Assis, et. al., 2003).
Three different shifts were examined: morning, afternoon, and night. The work tasks were the same for
the three shifts. The 66 total participants (n = 22 per shift) were asked to answer questionnaires regarding
their eating habits. Age, body weight and Body Mass Index (BMI) were similar for all three groups as
well as daily energy expenditure. Multiple regression analysis was used to compare factors influencing
energy and nutrient intake (De Assis, et. al., 2003).
It was found that energy intake did not differ among the three shifts, even with differences in food
choices and ingestion times (De Assis, et. al., 2003). Factors that were found to have a significant impact
on ingestion included hour of day, time since last meal, age, and BMI (De Assis, et. al., 2003). When
looking at macronutrients, only carbohydrate intake significantly differed among the three shifts. Soda
consumption was high among all shifts, with night workers consuming the greatest amount of soda
compared to morning and afternoon workers (De Assis, et. al., 2003). This may be due to the caffeine or
high sugar content of sodas, which would help combat fatigue during the night shift. The results confirm
the influence of factors other than shift time on health and nutrition, including time of day, time since last
meal, age, and BMI (De Assis, et. al., 2003).
Similarly, Reeves, Newling-Ward, and Gissane (2004) investigated how shift work changes food
intake and eating patterns and the impact of these changes on health. It was found that night workers did
not consume more than day workers, but that they did eat smaller meals and snacks over more time.
Furthermore, night workers showed a more significant difference in their food intake patterns on work
3
days and days off, while day workers did not (Reeves, et. al. 2004). Shift work impacts the timing of food
consumption since there is typically less availability of food during the night shifts, which is thought to
contribute to unhealthy eating patterns observed in shift workers (Reeves, et. al., 2004).
Fisher, Rutishauser and Read (1986) investigated how the specific type of job, work environment,
and availability of food in the work place influence dietary habits among 25 oil refinery shift workers.
Day, night and afternoon shifts were investigated using a 24-hour food intake record. Significant
differences were found between the types and frequency of foods consumed during the different shifts
(Fisher, et. al., 1986). These differences were thought to be related to change in type of meal consumed as
well as the availability of food, with night shift workers being primarily influenced by the availability of
food and time constraints, rather than by hunger (Fisher, et. al., 1986).
Lennernas, Hambraeus, and Akerstedt (1995) explored how work hours impact eating behaviors
and dietary choices among 96 male industrial workers on day work (work 40h/week between 0654 and
1530 hours), two-shift work (work 38h/week between 0530 and 1400 hours one week and between 1400
and 2230 hours the next week), and three-shift work (work 36h/week between 0530 and 1400 hours, 1400
and 2230 hours, and 2230 and 0530 hours). Due to a lack of research connecting shift work, eating
behaviors, and disease as well as a gap in knowledge regarding shift workers and their specific food
intake, the researchers chose to evaluate how shift work influences eating habits and nutritional
parameters (Lennernas, et. al. 1995). Not only did the researchers compare energy and nutrient intake, but
they also considered coffee and tea consumption for 8-hour work shifts (day, morning, afternoon, night),
and for the 24-hour periods that included all four shifts. It was observed that the intake of coffee was
significantly lower among three-shift workers on days off compared to work days (Lennernas, et. al.
1995). Among all three groups of workers, a difference in food and beverage intake, including coffee and
alcohol, was found between work days and days off; however, no difference was reported among the 24-
hour work periods with varying work shifts (Lennernas, et. al. 1995). Overall, energy intake and quality
of food were not significantly influenced by shift work. Two- and three- shift work appeared to change
4
circadian distribution of food and coffee intake, but not the overall 24-hour consumption (Lennernas, et.
al. 1995).
Another aspect that has been shown to influence tolerance of shift work is aging, as studied by
Smith and Mason (2001). The number of shift workers aged over 45 years is increasing. It was
hypothesized that age is linked to decreased tolerance of shiftwork as it relates to aging, lifestyle,
behavior, type of work and the shift worked (Smith & Mason, 2001). The participants in this study
included 306 police officers working a new rotation for approximately six months. They were divided
into three age groups for comparison (1=20-32.9, 2=33-39.9, 3=40+). A range of sleep-related variables,
including fatigue, drowsiness, sleep quality, sleep duration, and caffeine consumption, were analyzed
using multivariate analysis of covariance (Smith & Mason, 2001). Results showed that the three age
groups differed significantly in terms of sleep needs (Smith & Mason, 2001). Younger officers tended to
report better shift work attitudes, better adjustment to night shifts, higher job satisfaction and commitment
to the work organization, less fatigue and longer sleep duration needs. Older officers reported
significantly higher “morningness” (morning-oriented) and lower sleep needs (Smith & Mason, 2001).
Concerning caffeine intake, older officers tended to consume more caffeine on all shifts worked (Smith &
Mason, 2001). Older officers also tended to report less drowsiness on morning compared to night shifts.
The results support the hypothesis that shiftwork tolerance is related to age; however, a link between the
impacts of aging, lifestyle, behavior, type of work and the rotation worked has yet to be fully understood.
Caffeine has taken an important role in helping to maintain performance among shift workers as
well as other sleep depriving situations. Snel and Lorist (2011) researched how caffeine can be used to
alter mental state. While caffeine has been found to be useful for restoring low levels of wakefulness, it
may result in detrimental effects on sleep, producing daytime fatigue (Snel & Lorist, 2011). During night
shift work, where sleep deprivation is common, caffeine contributes to maintaining performance and
wakefulness. However, the effects of caffeine may be due, in part, to caffeine expectancy and placebo
effects (Snel & Lorist, 2011). Disturbances in the circadian rhythm, such as those seen with shift work,
5
disrupt normal sleeping patterns and cognition, which may result in less than optimal work performance.
According to Snel and Lorist (2011), it is necessary to combat circadian disturbances and restore
cognitive performance, especially during work.
A series of double-blind studies was conducted by Killgore, Kahn-Greene, Grugle, Killgore, and
Balkin (2009) to determine whether or not caffeine is a viable intervention to restore cognition. Fifty-four
participants (18-36 years old) who had been sleep deprived for 45-50 hours were given either a 600 mg
dose of caffeine, 400 mg modafinil (approved by FDA to manage fatigue, sleep apnea, and shift work
sleep disorder), 20 mg dextroamphetamine, or a placebo prior to completing various cognitive tests
designed to measure formation of abstract concepts (Killgore, et. al., 2009). Results showed that the three
different stimulants (caffeine, modafinil, and dextroamphetamine) affected the outcome of the cognitive
tests differently, with caffeine affecting cognitive processing most significantly (Killgore, et. al., 2009).
Thus, it is suggested that performance is influenced by sleep deprivation and that this deprivation can be
counteracted, in part, by caffeine (Killgore, et. al., 2009).
In another study, Kilpelaiinen, Huttunen, Lohi, and Lyytinen (2010) focused on perceived
effects of caffeine on sleepiness, motivation, mood, and task performance. Military pilot students (23-24
years old) underwent a series of flight simulator tests after a 37-hour sleep deprivation period. It was
predicted that sleep deprivation would decrease amount of correct answers and increase reaction time
during testing. The subjects received either a placebo or 200 mg caffeine twice a day. Vigilance and
learning were assessed during the test (Kilpelaiinen, et. al., 2010). As expected, sleep deprivation
decreased the number of correct decisions as well as increased the reaction time for both placebo and
caffeine groups. Interestingly, the perceived feeling of success remained stable across sustained
wakefulness in the caffeine group (Kilpelaiinen, et. al., 2010). Similarly, it was observed by Ker, Edwards,
Felix, Blackhall, and Roberts (2010) that caffeine has a significant impact on reducing the number of
errors compared to a placebo.
6
Pecotic, Valic, Kardum, Seva, and Dogas (2008) further investigated the impact of caffeine on the
adaptability of sleep habits by shift workers. The researchers predicted that sleep habits would be
negatively affected by caffeine consumption. Medical students, physicians, specialists, and nurses
participated in this study. The variables found to influence the amount of sleep needed were age, gender,
work demands, and work schedule (Pecotic, et. al., 2008). Surprisingly, those participants who consumed
caffeine reported greater difficulty staying awake during lectures and while driving a car. Based on these
results, the researcher’s hypothesis that caffeine intake impairs sleep habits and quality of sleep was
supported.
Even in well-rested individuals, such as those examined in a study conducted by Michael, Johns,
Owen, and Patterson (2008), caffeine can reduce drowsiness that persists for 3-4 hours. Different results
between caffeine and placebo were only observed 30 minutes post administration of treatment. While this
supports the prediction that caffeine can beneficially impact alertness and work performance, the benefits
may be short-lived (Michael, et. al., 2008). Furthermore, the findings from Snel and Lorist (2011) show
that caffeine can alter mental state in terms of cognitive processing and alertness as caffeine has been
shown to restore wakefulness and performance. This is an important conclusion and may help explain the
use of caffeine during shift work as a means to counteract fatigue and cognitive dysfunction.
CHAPTER 2
MANUSCRIPT
CAFFEINE INTAKE AMONG LAW ENFORCEMENT OFFICERS PARTICIPATING IN SHIFT
WORK
Abstract
Objective: To investigate caffeine consumption among law enforcement officers during day shifts (DAY)
and night shifts (NIGHT); to assess the types of caffeine-containing products and the frequency of
product intake during DAY and NIGHT; to identify relationships between caffeine products and selected
demographic characteristics; to identify relationships between caffeine intake and job-related
characteristics.; and to identify associative relationships between caffeine intake and perceived
concentration level, caffeine side effects, and tobacco use.
Participants: Police officers (PO) and Sheriff Department Deputies (SDD) in a rural region in Eastern
North Carolina. Useable sample of 75 (N = 75).
Methods: Anonymous, self-administered caffeine food-frequency questionnaires (FFQ; three for DAY,
three for NIGHT) and demographic questionnaire. Nonparametric tests were used. The Wilcoxon signed
rank test was used to compare related samples (caffeine intake DAY to NIGHT, types of products used
DAY to NIGHT, and frequency of product use DAY to NIGHT). Spearman’s correlation was used to
determine relationships between two samples. The Mann-Whitney U test was used to compare two
independent samples. The Kruskal-Wallis test was used to compare more than two independent samples.
Results: Caffeine consumption was similar from DAY to NIGHT (225 + 227mg DAY; 252 + 232 mg
NIGHT; p=0.891). Frequency of tea intake was greater during DAY (p=0.032). Greater caffeine
consumption was found among (1) SDD versus PO; (2) expert officers (for night only); (3) lower
concentration levels (for night only); (4) tobacco users; (5) those who experience caffeine side effects. In
addition, younger officers consumed more energy drinks DAY and NIGHT.
8
Conclusions: It was found that certain job-related characteristics influence caffeine intake, but types of
products used and frequency of intake tends to be the same regardless of shift or caffeine side effects.
Energy drinks were found to be most popular among younger officers. Tobacco use and caffeine intake
were found to have a correlate relationship. Caffeine may aid alertness, especially at night. Further
research is needed to assess other areas of shiftwork, more caffeine-containing products, especially
energy drinks, and psychological or behavioral aspects of caffeine intake and product choice among shift
workers. Limitations, gaps in the literature and implications are discussed.
9
CAFFEINE INTAKE AMONG LAW ENFORCEMENT OFFICERS PARTICIPATING IN SHIFT
WORK
Introduction
Participation in shift work has been found to interfere with normal behaviors, including eating,
exercise, and regular sleep patterns. Disruption in these normal behaviors can lead to adverse health
effects. Shift work is associated with increased BMI and prevalence of obesity, cardiovascular disease,
peptic ulcers, and gastrointestinal dysfunction (Atkinson, 2008; Holmback, 2002). De Assis and
colleagues (2003) observed that night shift workers are more likely to consume energy-dense foods and
beverages during shift work compared to morning and afternoon workers (2003). Fisher and colleagues
(1986) reported that the types and frequency of foods consumed during the night shift are influenced by
the availability of food and time constraints, rather than by hunger.
Disturbances in sleep patterns and normal circadian rhythms may contribute to food and beverage
choices among shift workers (Atkinson, et al., 2008). According to Snel and Lorist (2011), caffeine is
commonly used to offset fatigue and sleepiness and to assist with maintaining work performance.
Lennernas and colleagues (1995) studied the impact of work hours on eating behaviors and dietary
choices among male industrial workers on day work (work 40h/week between 0654 and 1530 hours),
two-shift work (work 38h/week between 0530 and 1400 hours one week and between 1400 and 2230
hours the next week), and three-shift work (work 36h/week between 0530 and 1400 hours, 1400 and 2230
hours, and 2230 and 0530 hours). The findings indicated that intake of coffee was significantly higher
among three-shift workers on work days compared to days off. Among all groups, differences in food and
beverage intake, including coffee and alcohol, were found between work days and days off (Lennernas,
Hambraeus, & Akerstedt, 1995).
According to Schardt (2012), over 80 percent of American adults regularly consume caffeine.
Burke (2008) states that 90 percent of adults worldwide consume caffeine daily. Some of the most
common caffeine-containing beverages include coffee, tea, and soda, as assessed by Barone and Roberts
10
(1996). Both American adults and adolescents consume caffeine, but caffeine intake has been shown to be
nearly three times greater among the adult population (Barone & Roberts, 1996). Energy drinks have been
declared the second most popular supplement behind multi-vitamins, specifically among adolescents and
young adults (Hoffman, 2010). In addition, caffeine-containing products are commonly used among
athletes due the perceived effects on performance and endurance (Hoffman, 2010; Desbrow & Leveritt
2006; Burke, 2008). Athletes that competed in the 2005 Ironman triathlon chose sodas, caffeinated gels,
coffee, energy drinks, and NoDoz tablets most often (Desbrow & Leveritt, 2006).
Caffeine effects people differently, with reported side effects ranging from jittery, nervous, and
restless to energetic or insomniatic (Monroe, 1998). Use of caffeine can result in dependence, defined as
craving caffeine regularly, and tolerance, defined as the body requiring more and more caffeine to obtain
the same effect (Monroe, 1998). Along with dependence comes withdrawal, which often results in
increased negative mood (Monroe, 1998; Smith, 2002). Caffeine side effects are more common among
heavy daily caffeine users and include insomnia, restlessness, depression, rapid heartbeat, anxiety,
stomach pain or heartburn, headaches, muscle gain, shaking, and ringing in the ears (Monroe, 1998).
Some of the more positive side effects are enhanced wakefulness and alertness, which have attracted shift
workers, truck drivers, military officers, athletes, and others who aim to combat fatigue (Burke, 2008).
While the majority of adults both worldwide and within the United States regularly use caffeine,
it has been found to be most popular among certain populations, including athletes (Hoffman, 2010;
Desbrow & Leveritt 2006; Burke, 2008) and night workers (Smith, 2002). Studies have shown caffeine
intake to be common among various workers participating in night shifts, including industrial workers,
electronic workers, steel and railway workers, and medical staff, including nurses (Glazner, 1991).
Lieberman and colleagues (2012) assessed intake among US army soldiers, where coffee was the most
popular product used overall, and energy drinks was most commonly used among young adult males.
Literature is lacking on caffeine intake among law enforcement officers participating in shift work as well
as among shift workers serving as their own control to compare day versus night shifts.
11
The purpose of this study was to evaluate caffeine intake from caffeinated beverages and energy
boosting supplements among law enforcement officers while working day shifts (DAY) and night shifts
(NIGHT). The specific research aims for this study were: 1) Assess the amount of caffeine consumed, the
types of caffeine products used, and the frequency of caffeine products used between DAY and NIGHT; 2)
Identify relationships between caffeine products used and demographic characteristics; 3) Identify
relationships between caffeine intake and job-related characteristics; and 4) Identify associative
relationships between caffeine intake and perceived concentration level, caffeine side effects, and tobacco
use.
Methods
This was a quantitative prospective study design. Participants were Police Officers (PO) and
Sheriff Department Deputies (SDD) of a rural city in the eastern region of North Carolina who worked
rotating DAY and NIGHT. The county served was 652 square miles large with a population of 172,554
(United States Census Bureau, 2012). The city served accounted for 5% of the county (34.6 sq. mi.) and
over half of the county population (population 87,242), making the target city the largest, most populated
area of the county (United States Census Bureau, 2012). In 2012, the county index crime rate was
3,986.5 per 100,000 persons, which was slightly higher than the state index crime rate of 3,767.2 per
100,000 persons (State Bureau of Investigation, 2013).
Caffeine Food Frequency Questionnaire (FFQ)
A quantitative self-administered caffeine food frequency questionnaire (FFQ) was used to assess
24-hour caffeine intake from beverages and supplements for each of three DAY and NIGHT shifts
worked. An initial FFQ with five drink categories was designed from the literature (Neuhours et al, 2009;
Heckman et al, 2010; McCusker et al, 2003; Chin et al, 2008; USDA, 2011; McCusker et al, 2006; Chou
et al, 2007; McCusker 2006; Consumer Reports 2011; (Starbucks Drinks., 2012; McDonald’s Beverages,
2012). Additional drink categories and an energy-boosting supplement category were added following
field research by a Registered Dietitian of area convenient stores, pharmacies, coffee shops, and grocery
12
stores. The final FFQ had seven drink and four energy boosting supplement categories. Drink categories
included coffee, coffee drinks, espresso, caffeinated sodas, energy drinks, tea, and caffeinated water. The
energy boosting supplements included energy shots, caffeine supplements, energy chewing gum, and
energy strips. Participants indicated each drink and supplement that they consumed for the given shift, in
addition to serving size and number of servings consumed for each item. A reference serving size was
provided for each item. For example, eight ounces (8 oz.) for coffee (standard coffee mug size) and 12 oz.
for soda (regular can of soda). Job-related information for that shift was also reported, including the shift
worked (day or night), the number of hours worked, and how busy was the shift (not busy, slightly busy
but less than normal, normal workload, slightly more busy than normal, or very busy).
The FFQ was content validated by two Registered Dietitians, the SD Captain, and a Health
Educator. Revisions were made to drink categories and serving sizes from feedback provided. A pilot
test was next conducted among five university fitness center employees, aged 22 to 40 years (two males
and three females). Reference serving sizes for each drink were added and modifications to heading titles
and instructions for completing the questionnaire were made from feedback provided. Table 1 presents
the mean caffeine content (mg) of products assessed on the FFQ. A serving size reference sheet was
developed from field research of area restaurants. The sheet listed standard serving sizes for each drink
and supplement.
Demographic and Caffeine Effects Questionnaire
The Demographic and Caffeine Effects Questionnaire assessed demographic information and
characteristics of shift work for the past two weeks. As part of this questionnaire, a 7-point Likert scale
was used to assess perceived level of energy and concentration while participating in shift work (1 =
extremely low, 7 = extremely high; Laerhoven, et al, 2004). The questionnaire also assessed side effects
experienced from consuming caffeine. This questionnaire was reviewed for content validity by the SD
Captain, a Health Educator, and the City Human Resources Safety and Risk Manager. Three additional
job-related and two demographic questions were added from their feedback. Further, the phrasing of the
13
Likert scale question was modified. The study was approved by the university’s Institutional Review
Board, the SD Captain, the Chief of Police, and the City Human Resources Safety and Risk Manager. All
participants provided written informed consent prior to study participation. Data collection took place
from April to June 2012.
Procedures
Each participant received a 9-page packet. Page 1 included a description of each item in the
packet, deadline for completion, and researcher contact information. Page 2 was the Demographic and
Caffeine Effects Questionnaire. Pages 3-8 were color-coded FFQs (pages 3-5 were yellow for DAY;
pages 6-8 were purple for NIGHT). Page 9 was the Serving Size Reference Sheet, which included a
graphic outline of a 16-ounce cup for gauging drink size. To maintain participant anonymity, each page
was ID-coded with the same randomly assigned three-digit number.
Initially, the researcher attended roll call for each patrol unit of the SD. The researcher used a
script to describe the packet content and time frame for completing the study, instruct recruits that
participation was voluntary and recorded anonymous, and to obtain informed consent. Participants
returned the packets to an assigned deputy; these were then given to the Captain and returned to the
researcher. The same protocol was used for recruiting PO, with the exception that an assigned PO
distributed and collected the packets, which were then returned by the PO to the researcher. By telephone
and email, the researcher was in contact with the SDD and PO bi-monthly throughout data collection.
Neither questions nor concerns were reported throughout the data collection period.
Statistical Analysis
Because data were not normally distributed, nonparametric tests were used for analyses. The
Wilcoxon Signed Rank Test for related samples was used to compare mean caffeine intake and frequency
of product intake for DAY and NIGHT in order to assess mean population difference (Wilcoxon, 1945).
Spearman’s correlation was used to assess the significance of the relationship between two quantitative
variables (Page, 1963). The Kruskal-Wallis test was used when comparing more than two independent
14
samples (Kruskal, Wallis, 1952), and The Mann-Whitney U Test for independent samples was used
(Lund Research Limited, 2013) (Page, 1963) for comparisons between two non-related samples.
Results
There were 187 PO who serviced the city area. The PO operated 12-hour shifts and rotated from
DAY to NIGHT (and vice versa) every three weeks. There were 199 SDD who serviced the county
beyond police jurisdiction (City of Greenville, 2007; Pitt County Development Commission, 2007-2012).
SDD also had 12-hour shifts, but rotated shifts every four weeks. SDD followed a Dupont schedule;
officers worked two days during the week, then had two days off, and worked every other weekend
(Friday, Saturday, and Sunday) for a total of seven 12-hour shifts during a 2-week period. There were
111 packets distributed between the PO and SDD. The response rate was 76% and 9 were excluded,
resulting in a 68% useable response rate. Those who returned only one FFQ for DAY or NIGHT were
excluded.
Participant demographic characteristics are reported in Tables 2 and 3. Mean participant age was
36 years, and ranged from 23 to 58 years (24% aged 20-29 years, 39% were 30-39 years, 30% were 40-49
years, and 7% were 50-58 years). The majority was male, married, non-Hispanic white, and did not use
tobacco. On average, participants were in their current position for the past six years and in shift work for
the past 10 years. The majority changed shift each three weeks (PO) and worked overtime 2 to 6 of the
past 14 days.
Assessment of Caffeine Consumption
Overall, daily caffeine intake and types of caffeine products used were similar between DAY and
NIGHT (255±227 milligrams per day (mg/d) (M±SD), Mdn 180 mg/d, 95% CI [202, 308] mg/d DAY;
252±232, 164, [198, 306] NIGHT; p=0.891), as assessed using the Wilcoxon Signed Rank Test.
Frequency of product consumption was similar between DAY and NIGHT for all products except tea,
which had greater frequency during DAY (p=0.032). The number of days and mean frequency of product
intake for 3-day shift periods are reported in Tables 4 and 5. Of the four supplements assessed, energy
15
shots was the only one that was used by participants. The other products assessed included NoDoz,
energy gum and energy strips.
Caffeine Product Use and Demographic Characteristics
Demographic characteristics tested in this study included tobacco use, age, sex, marital status,
and race. Energy drinks was the only product that differed with age. Only eight of the 75 participants used
energy drinks; their ages ranged from 24 to 40 years. Those with the highest intake of energy drinks
(average one per shift) were younger for both DAY and NIGHT. Table 6 presents the mean distribution of
ages for energy drink consumption during DAY and NIGHT. Mean caffeine intake and product
consumption were similar across categories of sex, marital status, and race (data not shown).
Further looking at demographic characteristics between departments, SDD were, on average, five
years older than PO (34±8 years (M±SD), Mdn 33 years, 95% CI [32, 37] years for PO; 39±7 years, 39
years, [36, 41] years for SDD; p=0.016) using the Kruskal-Wallis Test. Gender, marital status and race,
were similar between departments (data not shown).
Caffeine Intake and Job Related Characteristics
Job-related characteristics assessed included shifts worked (8-10 hour night or day versus 12 hour
night or day), length of time participating in current position and in shift work overall, how often the shift
changed from day to night, perceived energy and concentration levels during shift work, caffeine side
effects, overtime, and department. As illustrated in Table 7, mean caffeine intake was nearly twice as
much for SDD versus PO for both DAY (p=0.001) and NIGHT (p=0.001). However, within each
department, caffeine intake was similar between DAY and NIGHT (p=0.206 SD; p=0.258 PO). Those
who worked more years of shift work (>10 years = “expert”; N= 29; Marsal, 2013) consumed more
caffeine during NIGHT (p=0.029) regardless of department, as illustrated in Figure 1. Intake was similar
based on years of shift work for DAY (p=0.077). In addition, caffeine intake was similar despite years
worked at current job, overtime, how busy the current shift was, or the reported hours worked for that
shift (data not shown).
16
Associative Relationship between Caffeine Intake, Perceived Concentration Level, Caffeine Side Effects
and Tobacco Use
Associative relationships between caffeine intake and tobacco use, side effects from caffeine
ingestion, and perceived level of concentration during shift work are reported in Table 8. Tobacco users
consumed about 190 mg/d more caffeine for DAY and NIGHT as compared to those who did not use
tobacco. Further, those who had caffeine side effects consumed about 130 mg/d more caffeine during
NIGHT and 80 mg/d more during DAY as compared to those without side effects. As displayed in
Figures 2 and 3, for both DAY and NIGHT the majority of participants who reported no caffeine side
effects consumed less caffeine (between 0-200 mg) and a weak negative trend between caffeine intake
and age was found; thus, as caffeine intake increased, age decreased. In contrast, among participants who
did report caffeine side effects, caffeine intake was widely distributed and a weak positive correlation
between caffeine intake and age was seen; thus, as caffeine intake increased, age also increased.
Concentration level approached statistical significance for NIGHT only (p=0.056), with a greater mean
caffeine intake observed for lower concentration. Intake was similar for DAY and NIGHT across reported
energy levels (data not shown).
Discussion
Among a survey of law enforcement officers, the results of this study indicate that certain job-
related factors influence caffeine consumption, including department (SDD versus PO), perceived
concentration level and the number of years participating in shift work. Nearly double the intake of
caffeine was found among SDD versus PO, regardless of shift. Whereas caffeine consumption and
frequency of caffeine product use were similar between DAY and NIGHT, those who consumed more
caffeine reported caffeine side effects, including headaches, stomach aches, heart palpitations, heart burn,
and “jolt and crash” episodes.
These results suggest that habits play a large role in caffeine consumption regardless of shift.
Regular caffeine use has been recognized as habit-forming and strongly addictive, as well as being
17
associated with withdrawal symptoms (Smith, 1987; Olekalns & Bardsley, 1996). Tobacco use is also
habit-forming (Stolerman & Jarvis, 1995), which may explain why many who use tobacco also consume
more caffeine. Since the majority of participants consumed the same amount of caffeine and caffeine-
containing products for DAY and NIGHT, availability does not seem to be a factor. All of the products
assessed are readily available at convenience stores, grocery stores and fast food restaurants; many of
these establishments are open 24-hours per day or have extended hours of operation.
Higher caffeine intake for NIGHT was associated with reported lower concentration level.
Working night shifts disrupts the normal circadian rhythm (Van Reeth, 1998). Even after years of shift
work, it has been reported that circadian rhythm does not adjust to working nighttime hours (Van Reeth,
1998). The wide use of caffeine to combat sleepiness and circadian disturbances (Roehrs & Roth, 2008)
may explain why a greater caffeine intake was reported at night among those with lower concentration
levels and for those who participated in shift work for more years. There may be other contributing
factors that impact caffeine intake and the choice of products that contain caffeine that were not assessed
in the current study, such as co-worker influence, stress of the job, down time, location of patrol work (i.e.,
in city limits versus rural areas), and availability of products within the workplace.
Previous studies have found that food choices and frequency of intake are influenced more by
habit and time availability rather than by hunger (Waterhouse, Buckley, Edwards, & Reilly, 2003).
Waterhouse and colleagues (2003) found a higher intake of snacks and lower intake of large hot meals
among night workers compared to day workers. Multiple researchers have reported that total energy
intake for shift workers does not differ despite the shift worked (Debry, et al, 1967; Reinberg, et al, 1979;
Pasqua & Moreno, 2004; Lennernas, et al, 1994), which is consistent with our findings that caffeine
consumption was similar between DAY and NIGHT. Lowden and colleagues (2010), however, found that
eating behaviors are influenced by night shift work. Psychological disruptions in eating habits, including
meal times of shift workers compared to culturally normal meal times, have been associated with
18
increased snacking among night shift workers (Lowden, et al, 2010). Thus, further research should
investigate timing of caffeine and caffeine-product intake among shift workers.
In contrast to the present study, Reeves and colleagues found that female night-shift workers
consumed more cups of coffee and tea compared to female and male day-shift workers. These
investigators also found that night workers smoked more cigarettes than day workers (Reeves, et al, 2004).
Both caffeine and nicotine likely aid alertness and are habit-forming (Smith, 1987; Olekalns & Bardsley,
1996; Stolerman & Jarvis, 1995; Roehrs & Roth, 2008), which may explain why night shift workers
consumed more of both. Similar to our findings, a strong relationship between tobacco use, namely
cigarette smoking, and caffeine consumption has been reported (Friedman, et al, 1974; Dawber et al, 1974;
Prineas, et al, 1980; Kannas, 1981; Thomas, 1973; Conway, et al, 1981). Roehrs and Roth (2008) reported
that caffeine is a widely used substance among those with sleep deprivation, restriction, or circadian
rhythm reversal to combat sleepiness because it enhances alertness and performance. The present study
found that a lower reported concentration level was associated with greater caffeine intake at night, which
may be explained by caffeine’s ability to offset fatigue and restore performance. Weis and Laties (1962)
concluded that caffeine restored numerous performance tasks while Smith (2002) further reported that
caffeine improved attention and psychomotor functions as opposed to complex intellectual tasks.
Although age did not influence overall caffeine intake, it does seem to be a determinant toward
chosen caffeine products. Our findings suggest that energy drinks are more popular among younger
officers in their early-to-mid-twenties. Energy drinks have become a popular product in recent years,
especially among teens and young adults (Torpy & Livingston, 2013; Rath, 2012; Hoffman, 2010).
Energy drinks are considered the second most popular supplement, after multi-vitamins, among American
adolescents and young adults. Caffeine is the primary active ingredient in energy drinks (Hoffman, 2010).
In addition, many energy drinks combine caffeine with other stimulatory ingredients (Hoffman, 2010).
Young adult males are the targeted market for energy drinks due to the reported ability of energy drinks to
19
enhance performance, increase attention, and prolong endurance (Reissig, et al, 2008). These performance
effects might be considered beneficial among young male shift workers.
A limitation of the current study was that intake of caffeine-containing products was self-reported.
The researchers did not control the products consumed, and were not able to determine whether or not
reported sizes were consistent with the sizes provided on the FFQ. It has been commonly found that self-
reported food intake underestimates actual intake (Gibson, 2005). Similarly, participants may have
underestimated the size of a product consumed. Although the FFQ included many caffeine-containing
beverages and supplements, it is possible that more products were available. In addition, most of the
participants were male, limiting the researchers to distinguish between sex differences with regard to
caffeine intake.
Future research should compare the present study’s findings with other areas of shift work as well
as other regions of the country and urban areas of residency. Future studies may better determine whether
the length of time during a shift as well as how often one rotates from day to night shifts influences
caffeine intake, since both participating departments in the present study worked 12-hour shifts and
rotated from day to night in relatively the same amount of time (every three versus four weeks). Caffeine-
containing products offered on the market today will change as new products become available and grow
in popularity; therefore, it is important for future research to identify the variety of caffeine-containing
products on the market. Energy drinks are one such product that continues to gain popularity for its
perceived energy-boosting and performance-enhancing properties (Rath, 2012; Hoffman, 2010; Reissig,
et al, 2008). Future research with a greater focus on energy drink consumption among shift workers is
needed. Psychological aspects seem to play a significant role in determining food choices (Lowden, et al,
2010); however, there is a lack of research investigating the relationship between caffeine consumption
and psychological factors among shift workers. Additional research aiming to explore this relationship is
indicated to better understand caffeine intake among shift workers from a behavioral standpoint. Finally,
there may be other factors that influence caffeine consumption among shift workers, such as number of
20
children in the participants’ household and children’s ages, health status of the participant, and how the
participant perceives caffeine with relation to health or nutritional value.
21
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LIST OF TABLES
TABLE 1. Mean caffeine content (mg) per serving of caffeine-containing products assessed
on the food frequency questionnaire as reported by various sources
TABLE 2. Age and job-related characteristics of Police and Sheriff law enforcement
officers
TABLE 3. Demographic characteristics of Police and Sheriff law enforcement
officers
TABLE 4. Number of days or nights during a 3-day shift that caffeine-containing products were
consumed among Police and Sheriff officers as reported by food frequency questionnaire
TABLE 5. Mean caffeine-containing product frequency during day and night shifts among Police
and Sheriff officers as reported by food frequency questionnaire
TABLE 6. Mean distribution of ages for energy drink consumption during day and night shifts
TABLE 7. Caffeine intake between Police and Sheriff departments for 3-day period of day and night
shifts as reported by food frequency questionnaire
TABLE 8. Associative relationships between caffeine intake and tobacco use, caffeine side effects,
and perceived concentration level among Police and Sheriff officers
28
LIST OF FIGURES
Figure 1. Caffeine intake during a 3-night shift period among “rookie,” “seasoned,” and “expert” Police
and Sheriff officers
Figure 2. Association of age and caffeine intake for those with caffeine side effects vs. those without side
effects for day shifts among Police and Sheriff officers
Figure 3. Association of age and caffeine intake for those with caffeine side effects vs. those without side
effects for night shifts among Police and Sheriff officers
29
Table 1. Mean caffeine content (mg) per serving of caffeine-containing products assessed on the food
frequency questionnaire (FFQ) as reported by various sources.
Product
Mean caffeine content
Serving size
Coffee
148 mg
8 fl. oz.
Coffee drinks
138 mg
16 fl. oz.
Espresso
58 mg
1 fl. oz.
Caffeinated sodas
37 mg
12 fl. oz.
Energy drinks
64 mg
8 fl. oz.
Tea
38 mg
8 fl. oz.
Caffeinated water
45 mg
90 mg
16.9 fl. oz. of Avitae regular
16.9 fl. oz. of Avitae 90
Energy shots
207 mg
2 fl. oz.
Note. Caffeine content not included for NoDoz, energy gum and energy strips; these were included on the
FFQ, but were not used by participants. (Anonymous, 2011; Center for Science in the Public Interest,
2011; Chin, et al., 2008; Chou, et al., 2007; McCusker, et al., 2003; McCusker, et al., 2006; Avitae
Caffeinated Water, 2012)
30
Table 2. Age and job-related characteristics of Police and Sheriff officers (N=75)
Characteristic
M (+SD)
Mdn
95% CI
Age (years)
36 (+8.0)
36
[34.3, 38.0]
Years at current job
6 (+4.8)
5
[5.0, 7.2]
Years shift work
10 (+6.6)
9
[8.3, 11.3]
Overtime (days)
2 (+1.7)
1
[1.1, 1.9]
Note. M = mean; SD = standard deviation; Mdn = median; CI = confidence interval.
31
Table 3. Demographic characteristics of Police and Sheriff officers (N=75)
Characteristic
Total (n)
Distribution (%)
Sex
Male
Female
Missing
66
8
1
88
11
1
Marital status
Single
Married
Divorced
Missing
13
56
4
2
17
75
5
3
Race
Non-Hispanic White
Asian American
Hispanic/Latino
African American
Other
Missing
59
1
3
9
1
2
79
1
4
12
1
3
Shift change
Each three weeks (Police)
Each four weeks (Sheriff)
Missing
44
29
2
59
38
3
33
Table 4. Number of days or nights during a 3-day shift that caffeine-containing products were consumed
among Police and Sheriff officers as reported by food frequency questionnaire (FFQ) (n = 64)
3-day shift period
Number of days
Number of nights
M (+SD)
Mdn
95% CI
M (+SD)
Mdn
95% CI
P-value
Product
Coffee
1.2 (+1.4)
0.0
[0.8, 1.5]
1.2 (+1.4)
0.0
[0.8, 1.5]
0.904
Coffee drinks 0.1 (+0.3) 0.0 [0.0, 0.1] 0.1 (+0.2) 0.0 [0.0, 0.1] 0.655
Espresso 0.1 (+0.3) 0.0 [0.0, 0.1] 0.1 (+0.4) 0.0 [0.0, 0.2] 0.414
Soda 1.4 (+1.3) 1.0 [1.0, 1.7] 1.3 (+1.4) 1.0 [1.0, 1.7] 0.623
Energy drinks 0.2 (+0.6) 0.0 [0.0, 0.3] 0.2 (+0.7) 0.0 [0.0, 0.4] 0.180
Tea 0.8 (+1.2) 0.0 [0.5, 1.1] 0.7 (+1.0) 0.0 [0.4, 0.9] 0.110
Water 0.1 (+0.5) 0.0 [0.0, 0.2] 0.1 (+0.3) 0.0 [0.0, 0.1] 0.655
Energy shots 0.1 (+0.3) 0.0 [0.0, 0.1] 0.0 (+0.2) 0.0 [0.0, 0.1] 0.414
Note. M = mean; SD = standard deviation; Mdn = median; CI = confidence interval. The sample size
accounts for elimination of participants who completed less than three FFQs for DAY or NIGHT. The
Wilcoxon Signed Rank Test was used to determine the p-values.
34
Table 5. Mean caffeine-containing product frequency during day and night shifts among Police and
Sheriff officers as reported by food frequency questionnaire (FFQ) (n = 64)
Frequency per 3-day shift period
Day
Night
M (+SD)
Mdn
95% CI
M (+SD)
Mdn
95% CI
P-value
Product
Coffee
0.6 (+0.9)
0.0
[0.4, 0.8]
0.6 (+0.8)
0.0
[0.4, 0.8]
0.974
Coffee drinks 0.0 (+0.2) 0.0 [0.0, 0.1] 0.0 (+0.1) 0.0 [0.0, 0.1] 0.705
Espresso 0.0 (+0.1) 0.0 [0.0, 0.1] 0.0 (+0.1) 0.0 [0.0, 0.1] 0.414
Soda 0.9 (+1.5) 0.3 [0.6, 1.3] 1.0 (+1.7) 0.3 [0.6, 1.4] 0.585
Energy drinks 0.1 (+0.2) 0.0 [0.0, 0.1] 0.1 (+0.2) 0.0 [0.0, 0.1] 0.317
Tea 0.4 (+0.7) 0.0 [0.2, 0.6] 0.3 (+0.7) 0.0 [0.2, 0.5] 0.032
Water 0.0 (+0.3) 0.0 [0.0, 0.1] 0.0 (+0.1) 0.0 [0.0, 0.0] 0.655
Energy shots 0.0 (+0.1) 0.0 [0.0, 0.0] 0.0 (+0.1) 0.0 [0.0, 0.0] 0.414
Note. M = mean; SD = standard deviation; Mdn = median; CI = confidence interval. The sample size
accounts for elimination of participants who completed less than three FFQs for DAY or NIGHT. The
Wilcoxon Signed Rank Test was used to determine the p-values.
35
Table 6. Mean distribution of ages for energy drink consumption during day and night shifts (N=75)
Age (years)
Day
Night
N
M (+SD)
Mdn
95% CI
P-value
M (+SD)
Mdn
95% CI
P-value
Mean number of
energy drinks
0.00
67
36.7 (+7.8)
36.0
[34.8, 38.6]
37.0 (+7.8)
37.0
[35.1, 38.9]
0.33
4
39.0 (+1.4)
39.0
[26.3, 51.7]
0.03
32.3 (+6.9)
32.0
[21.2, 43.3]
0.003
1.00
4
24.3 (+1.5)
24.0
[20.5, 28.1]
25.0 (+1.8)
25.0
[22.1, 27.9]
Note. M = mean; SD = standard deviation; Mdn = median; CI = confidence interval. Spearman’s
Correlation was used to determine the p-values.
36
Table 7. Caffeine intake between Police and Sheriff departments for 3-day period of day and night shifts
as reported by food frequency questionnaire (FFQ) (N=75)
Mean daily caffeine intake for 3-day shift (mg/d)
Day
Night
M (+SD)
Mdn
95% CI
P-value
M (+SD)
Mdn
95% CI
P-value
Department
Police
176 (+62)
135
126, 225
184 (+184)
137
128, 240
Sheriff
381 (+259)
319
282, 479
0.001
361 (+260)
341
262, 460
0.001
Note. M = mean; SDI = standard deviation; Mdn = median; CI = confidence interval. The Kruskal-Wallis
Test was used to determine the p-values.
37
Table 8. Associative relationships between caffeine intake and tobacco use, caffeine side effects, and
perceived concentration level among Police and Sheriff officers (N=75)
Caffeine intake (mg/day)
Day
Night
N
M (+SD)
Mdn
95% CI
P-value
M (+SD)
Mdn
95% CI
P-value
Characteristic
Tobacco
Use
Do not use
20
54
397 (+198)
208 (+217)
338
139
304, 489
149, 267
0.000
388 (+227)
206 (+214)
353
137
282, 494
147, 264
0.001
Caffeine side effects
Yes
No
17
58
318 (+161)
239 (+240)
319
139
235, 401
176, 302
0.024
352 (+209)
223 (+230)
305
141
245, 460
163, 284
0.009
Perceived concentration level
3
4
5
6
7
3
35
18
12
4
300 (+260)
293 (+224)
226 (+233)
275 (+252)
133 (+143)
169
261
136
218
114
-346, 945
216, 370
110, 342
115, 434
-94, 360
0.160
426 (+467)
301 (+239)
202 (+196)
235 (+198)
124 (+148)
173
283
126
183
100
-733, 1586
219, 383
105, 300
109, 361
-122, 360
0.055*
Note. M = mean; SD = standard deviation; Mdn = median; CI = confidence interval. Side effects from
caffeine ingestion included headaches, jolt and crash episodes, heart palpitations, stomach ache and heart
burn (Malinauskas, et al, 2007); a 7-point Likert scale was used to evaluate perceived concentration level
(1 = extremely low concentration, 4 = normal level of concentration, 7 = extremely high level
concentration) (Laerhoven, et al, 2004). Spearman’s correlation, the Kruskal-Wallis test and Mann-
Whitney U Test were used to determine the p-values.
*For perceived concentration level and caffeine intake during night, using Spearman’s Correlation,
p=0.055, using Pearson Correlation, p=0.047, and using the Kruskal-Wallis Test, p=0.283. Spearman’s
Correlation was kept due to data was not normally distributed.
38
Figure 1. Caffeine intake during a 3-night shift period among “rookie,” “seasoned,” and “expert” Police
and Sheriff officers (N=75)
Note. Rookie: < 1 year service (N=3); seasoned: 1-10 years (N=41); expert: > 10 years (N=29); (J. Marsal,
personal communication, April 16, 2013). *p=0.029. The Kruskal-Wallis Test was used to determine the
p-value.
39
Figure 2. Association of age and caffeine intake for those with caffeine side effects vs. those without side
effects for day shifts among Police and Sheriff officers (n=75)
Note. Mean caffeine intake from food frequency record for three day shifts.
40
Figure 3. Association of age and caffeine intake for those with caffeine side effects vs. those without side
effects for night shifts among Police and Sheriff officers (n=75)
Note. Mean caffeine intake from food frequency record for three night shifts.
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APPENDIX A
IRB APPROVAL LETTER
Notification of Exempt Certification
From: Social/Behavioral IRB
To: Maddison Greaves
CC:
Brenda Bertrand
Date: 2/16/2012
Re: UMCIRB 11-001291
Caffeine Intake among Shift Workers
I am pleased to inform you that your research submission has been certified as exempt on
2/15/2012. This study is eligible for Exempt Certification under category #2.
It is your responsibility to ensure that this research is conducted in the manner reported in your
application and/or protocol, as well as being consistent with the ethical principles of the Belmont
Report and your profession.
This research study does not require any additional interaction with the UMCIRB unless there
are proposed changes to this study. Any change, prior to implementing that change, must be
submitted to the UMCIRB for review and approval. The UMCIRB will determine if the change
impacts the eligibility of the research for exempt status. If more substantive review is required,
you will be notified within five business days.
The UMCIRB office will hold your exemption application for a period of five years from the
date of this letter. If you wish to continue this protocol beyond this period, you will need to
submit an Exemption Certification request at least 30 days before the end of the five year period.
The Chairperson (or designee) does not have a potential for conflict of interest on this study.
APPENDIX B
PERMISSION LETTERS
Statement of Informed Consent
Included in this packet:
6 Caffeine Surveys:
o Complete 3 while on three separate day shifts (yellow paper).
o Complete 3 while on three separate night shifts (blue paper).
1 Demographic Survey (pink paper)
1 Drink size reference sheet
Please complete all six caffeine surveys and demographic survey within four weeks of receiving
this packet.
This survey is anonymous, so please do not write your name. Your participation in this research
is voluntary. You may choose not to answer any or all questions, and you may stop at any time.
There is no penalty for not taking part in or withdrawing from this research study at any
point. If you have any questions please feel free to contact me, Maddie Greaves, at 619-980-
3355, or the Office for Human Research Integrity (OHRI) at 252-744-2914 for questions about
your rights as a research participant.